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首页> 外文期刊>Mathematical Problems in Engineering: Theory, Methods and Applications >Outlier Detection of Light Buoy Telemetry and Telecontrol Data Based on Improved Adaptive ε Neighborhood DBSCAN Clustering
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Outlier Detection of Light Buoy Telemetry and Telecontrol Data Based on Improved Adaptive ε Neighborhood DBSCAN Clustering

机译：基于改进自适应ε邻域DBSCAN聚类的光浮标遥测和远程控制数据的异常检测

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摘要

In this paper, according to the water area of light buoy, the migration rule of light buoy in main channel is counted, and the frequency of light buoy passing through a certain position point in the process of migration is calculated, and the model is verified by buoy position data. An anomaly detection algorithm based on improved adaptive DBSCAN clustering is designed. The size of the ε neighborhood is adaptive according to the wind speed, wave height, and drift distance span of the water area where the light buoy is located. The experimental results show that the improved adaptive DBSCAN clustering algorithm can solve the problem that the common DBSCAN clustering algorithm takes the “hot” water area of the light buoy position or the most likely area in the light buoy migration process as the noise point.
机译：本文根据光浮标的水面积，计算主通道中的光浮标的迁移规则，并计算通过迁移过程中的某个位置点的光浮标的频率，验证模型 通过浮标位置数据。 设计了一种基于改进的自适应DBSCAN聚类的异常检测算法。 ε附近的尺寸是根据灯浮标所定位的水区域的风速，波高和漂移距离跨度自适应。 实验结果表明，改进的自适应DBSCAN聚类算法可以解决普通DBSCAN聚类算法在光浮标位置的“热”水面积或光浮标迁移过程中的“热”水域作为噪声点的问题。

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